Prediction of biodiesel physico-chemical properties from its fatty acid composition using genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{ALVISO:2020:Fuel,
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author = "Dario Alviso and Guillermo Artana and Thomas Duriez",
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title = "Prediction of biodiesel physico-chemical properties
from its fatty acid composition using genetic
programming",
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journal = "Fuel",
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volume = "264",
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pages = "116844",
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year = "2020",
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ISSN = "0016-2361",
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DOI = "doi:10.1016/j.fuel.2019.116844",
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URL = "http://www.sciencedirect.com/science/article/pii/S0016236119321982",
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keywords = "genetic algorithms, genetic programming, Biodiesel,
Fatty acid, Properties, Regression analysis",
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abstract = "This paper presents regression analysis of biodiesel
physico-chemical properties as a function of fatty acid
composition using an experimental database. The study
is done by using 48 edible and non-edible oils-based
biodiesel available data. Regression equations are
presented as a function of fatty acid composition
(saturated and unsaturated methyl esters). The
physico-chemical properties studied are kinematic
viscosity, flash point, cloud point, pour point (PP),
cold filter plugging point, cetane (CN) and iodine
numbers. The regression technique chosen to carry out
this work is genetic programming (GP). Unlike multiple
linear regression (MLR) strategies available in
literature, GP provides generic, non-parametric
regression among variables. For all properties
analyzed, the performance of the regression is
systematically better for GP than MLR. Indeed, the RSME
related to the experimental database is lower for GP
models, from approx3percent for CN to approx55percent
for PP, in comparison to the best MLR model for each
property. Particularly, most GP regression models
reproduce correctly the dependence of properties on the
saturated and unsaturated methyl esters",
- }
Genetic Programming entries for
Dario Alviso
Guillermo Artana
Thomas Duriez
Citations